Title

Author

Date Awarded

Fall 2016

Document Type

Thesis

Degree Name

Master of Science (M.Sc.)

Department

Computer Science

Advisor

Evgenia Smirni

Committee Member

Weizhen Mao

Committee Member

Xu Liu

Abstract

Micro instances (t1.micro) are the class of Amazon EC2 virtual machines (VMs) offering the lowest operational costs for applications with short bursts in their CPU requirements. As processing proceeds, EC2 throttles CPU capacity of micro instances in a complex, unpredictable, manner. This thesis aims at making micro instances more predictable and efficient to use. First, we present a characterization of EC2 micro instances that evaluates the complex interactions between cost, performance, idleness and CPU throttling. Next, we define adaptive algorithms to manage CPU consumption by learning the workload characteristics at runtime and by injecting idleness to diminish host-level throttling. Experimental results show that a gradient-hill strategy leads to favorable results. For CPU bound workloads, we observe that a significant portion of jobs (up to 65%) can have end-to-end times that are even four times shorter than those of the more expensive m1.small class. Our algorithms drastically reduce the long tails of job execution times on the micro instances, resulting to favorable comparisons against even small instances.